Method and system to optimize pharmacotherapy

ABSTRACT

A method of optimizing pharmacotherapy includes monitoring one or more characteristics of a patient associated with a condition of the patient and evaluating a baseline severity of the condition of the patient, recommending a selected therapy to the patient based on a therapy efficacy-risk map, evaluating an actual severity of the condition of the patient, evaluating an efficacy of the selected therapy, evaluating a risk score of the selected therapy, and updating the therapy efficacy-risk map based on the evaluated efficacy and risk score of the selected therapy.

CROSS-REFERENCE TO PRIOR APPLICATIONS

This application claims the benefit of U.S. Provisional Application No. 63/054,197, filed on 20 Jul. 2020. This application is hereby incorporated by reference herein.

BACKGROUND OF THE INVENTION 1. Field of the Invention

The disclosed concept generally relates to therapy and, more particularly, to optimizing pharmacotherapy.

2. Description of the Related Art

Restless Legs Syndrome (RLS) has been estimated to impact between 3.9 to 14.3% of the adult population. Diagnosis of RLS requires the patient to express an urge to move the legs, typically associated with accompanying unpleasant sensations that begin to worsen as the patient remains still for an extended period of time. Typically the urge to move the legs is worse during the evening and night than during the day (most often at sleep onset) and is temporarily relieved by movement. RLS symptoms typically need to occur at least 2x/week and have significant side effects (e.g. daytime sleepiness, mood issues, etc.) in order to be considered for clinical treatment. The prevalence of RLS is significantly higher in women than in men and is a common disorder during pregnancy, especially toward the third trimester.

RLS can be treated with iron supplementation, dopamine agonists, alpha-2-delta calcium channel ligands, as well as non-pharmacologic interventions, like ensuring good sleep hygiene, avoiding caffeine, and getting regular low-intensity exercise. Also, avoiding exacerbating factors, like sleep deprivation or antidepressants.

Patients with milder symptoms that occur intermittently may be treated with non-pharmacologic therapy. For more severe symptoms, or when non-pharmacologic therapy is not successful, pharmacotherapy (i.e., drug therapy) is often used. Although there is continuing research on the best methods of treatment of RLS, the efficacy of pharmacotherapy tends to decrease over time and/or lead to augmentation. Lowered efficacy and augmentation may result in a physician altering drug dosing and/or dose timing, or changing the drug completely. Some subjectivity or trial and error may be involved in altering pharmacotherapy regimes for an RLS patient.

There remains room for improvement in pharmacotherapy for RLS or other conditions.

SUMMARY OF THE INVENTION

Accordingly, it is an object of the disclosed concept to provide a system and method to optimize pharmacotherapy treatment of conditions such as RLS or other conditions.

As one aspect of the disclosed concept, a method of optimizing pharmacotherapy comprises: monitoring one or more characteristics of a patient associated with a condition of the patient and evaluating a baseline severity of the condition of the patient; recommending a selected therapy to the patient based on a therapy efficacy-risk map; evaluating an actual severity of the condition of the patient; evaluating an efficacy of the selected therapy; evaluating a risk score of the selected therapy; and updating the therapy efficacy-risk map based on the evaluated efficacy and risk score of the selected therapy.

As one aspect of the disclosed concept, a system for optimizing pharmacotherapy comprises: one or more sensing modules structured to monitor one or more characteristics of a patient associated with a condition of the patient; a baseline severity module structured to determine a severity of the condition of the patient; a recommendation module structured to recommend a selected therapy to the patient based on a therapy efficacy-risk map; an actual severity evaluation module structured to evaluate an actual severity of the condition of the patient; a therapy efficacy evaluation module structured to evaluate an efficacy of the selected therapy; a risk evaluation module structured to evaluate a risk score of the selected therapy; and a therapy efficacy-risk map update module structured to update the therapy efficacy-risk map based on the evaluated efficacy and risk score of the selected therapy.

As one aspect of the disclosed concept, a non-transitory computer readable medium storing one or more programs, including instructions, which when executed by a computer, causes the computer to perform a method of optimizing pharmacotherapy. The method comprises: monitoring one or more characteristics of a patient associated with a condition of the patient and evaluating a baseline severity of the condition of the patient; recommending a selected therapy to the patient based on a therapy efficacy-risk map; evaluating an actual severity of the condition of the patient; evaluating an efficacy of the selected therapy; evaluating a risk score of the selected therapy; and updating the therapy efficacy-risk map based on the evaluated efficacy and risk score of the selected therapy.

These and other objects, features, and characteristics of the disclosed concept, as well as the methods of operation and functions of the related elements of structure and the combination of parts and economies of manufacture, will become more apparent upon consideration of the following description and the appended claims with reference to the accompanying drawings, all of which form a part of this specification, wherein like reference numerals designate corresponding parts in the various figures. It is to be expressly understood, however, that the drawings are for the purpose of illustration and description only and are not intended as a definition of the limits of the invention.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a flowchart of a method of optimizing pharmacotherapy in accordance with an example embodiment of the disclosed concept;

FIG. 2 is schematic diagram of a system for optimizing pharmacotherapy in accordance with an example embodiment of the disclosed concept;

FIG. 3 is a schematic diagram of a system in a build phase for evaluating risk of pharmacotherapy in accordance with an example embodiment of the disclosed concept; and

FIG. 4 is a schematic diagram of a system in a deployment phase for evaluating risk of pharmacotherapy in accordance with an example embodiment of the disclosed concept.

DETAILED DESCRIPTION OF EXEMPLARY EMBODIMENTS

As required, detailed embodiments of the disclosed concept are disclosed herein; however, it is to be understood that the disclosed embodiments are merely exemplary of the invention, which may be embodied in various forms. Therefore, specific structural and functional details disclosed herein are not to be interpreted as limiting, but merely as a basis for the claims and as a representative basis for teaching one skilled in the art to variously employ the disclosed concept in virtually any appropriately detailed structure.

As used herein, the singular form of “a”, “an”, and “the” include plural references unless the context clearly dictates otherwise.

Directional phrases used herein, such as, for example and without limitation, top, bottom, left, right, upper, lower, front, back, and derivatives thereof, relate to the orientation of the elements shown in the drawings and are not limiting upon the claims unless expressly recited therein.

In accordance with an embodiment of the disclosed concept, optimized pharmacotherapy recommendations are provided. For example, a personalized therapy efficacy-risk map is generated to recommend therapies corresponding to the severity of the patient's condition. Risks and efficacies of the therapies are included in the therapy efficacy-risk map. In building the personalized therapy efficacy-risk map, characteristics of a patient are continuously monitored and the risk and efficacy of treatments are evaluated. As the patient is continuously monitored and therapies are tried, the personalized efficacy-risk map is updated to reflect efficacies and risks of the therapies, as well as changes in efficacies and risks of the therapies. In this manner, pharmacotherapy efficacy and risk reduction can be optimized.

FIG. 1 is a flowchart of a method of optimizing pharmacotherapy in accordance with an example embodiment of the disclosed concept. The method begins at 100 where a number of behaviors and/or biometrics of a patient are monitored. The monitored behaviors and/or biometrics may include RLS objective metrics such as, without limitation, leg movement frequency, intensity, and duration. The monitored behaviors and/or biometrics may include sleep metrics such as, without limitation, sleep efficiency, wake after sleep onset, sleep onset latency, total sleep time, a number of awakenings, and sleep stages. The monitored behaviors and/or biometrics may include alertness metrics such as, without limitation, nap frequency, nap duration, movement, movement intensity, and behaviors. The monitored behaviors and/or biometrics may include behaviors such as, without limitation, sleep hygiene, caffeine intake frequency and timing, and medication intake. The monitored behaviors and/or biometrics may be monitored with one or more sensors such as, without limitation, under mattress and/or wearable sensors. The monitored behaviors and/or biometrics may also be monitored by patient provided subjective responses. The patient response may be gathered through any suitable means such as, without limitation, a digital survey, a chat bot, or any other suitable means for gathering subjective responses.

As part of the monitoring behaviors and biometrics, a severity of the patient's RLS may be determined. In an embodiment, the severity may be defined by pre-determined levels (i.e., mild, moderate, severe, and very severe) based on the International RLS Rating Scale. Objective RLS related physiological measures such as movement intensity, duration, and frequency may be used to determine the patient's RLS severity level.

At 102 a therapy is recommended to the patient. As part of recommending a therapy, a therapy efficacy-risk map is generated. The therapy efficacy-risk map corresponds a therapies to the severity of the patient's condition. Additionally, the therapy efficacy-risk map provides an efficacy and a risk of the therapy. An example therapy efficacy-risk map is provided below in Table 1 and an example of a more detailed therapy efficacy-risk map is provided below in Table 2.

TABLE 1 RLS Severity Therapy Past Efficacy Past Risk Score Level ID (F₁) (F₂) very severe 7 78 10 very severe 9 65 5 moderate 4 80 15 moderate 11 70 15 moderate 2 50 5 severe 1 68 15 severe 8 75 25 severe 6 60 20 mild 3 90 20 mild 5 84 16 mild 10 77 20

TABLE 2 RLS Past Severity Therapy Past Risk Dose Dose Level ID Efficacy Score Drug mg/d Time Side Effects Augmentation very 7 78 10 Pramipexole 0.25 8:00 PM none mild severe very 9 65 5 Pramipexole 0.5 7:00 PM somnolence mild severe moderate 4 80 15 Ropinirole 0.5 8:00 PM none none moderate 11 70 15 Gabapentin 600 mg 8:00 PM somnolence, mild enacarbil dizziness moderate 2 50 5 Ropinirole 0.25 8:00 PM nausea moderate severe 1 68 15 Ropinirole 0.25 8:00 PM nausea mild severe 8 75 25 Pramipexole 0.25 7:00 PM somnolence moderate severe 6 60 20 Ropinirole 0.5 7:00 PM nausea moderate mild 3 90 20 Ropinirole 0.25 8:00 PM none none mild 5 84 16 Ropinirole 0.5 7:00 PM nausea none mild 10 77 20 Gabapentin 600 mg 8:00 PM none mild enacarbil

As shown in the example therapy efficacy-risk map, each severity level includes a number of corresponding therapies (e.g., very severe has corresponding therapy IDs 7 and 9). In an example embodiment, each therapy ID corresponds to a particular drug, dose, and dose time. For example, therapy ID 7 corresponds to a 0.25 mg/d dose of Pramipexole at 8:00 PM and therapy ID 9 corresponds to a 0.5 mg/d dose of Pramipexole at 7:00 PM. Each therapy also has a corresponding efficacy and risk score. The efficacy of score represents the efficacy of the therapy and the risk score represents risks such as side effects and augmentation. Evaluation of the efficacy and risk of therapies will be described in more detail. When initially generated, the therapy efficacy-risk map may use population data such as clinical study results, review articles, or questionnaires to generate the efficacy and risk scores of therapies. However, as will be described in more detail herein, through continuous monitoring of the patient through various therapies, the therapy efficacy-risk map is updated to be personalized to the patient. For example, the efficacy and risk scores of therapies may be evaluated and updated based on patient monitoring.

In an embodiment, the therapy recommended to the patient will be selected from the therapies corresponding to the severity of the patient's condition. The selected therapy will maximize efficacy while minimizing the risk score. In an embodiment, to maximize efficacy while minimizing the risk score, a composite cost function may be used such as F(x)=W₁*F₁(x)−W₂F₂(x) where x is a vector in the solution space (i.e., it includes medication, dosing, dosing timing and duration), F₁(x) is the corresponding efficacy of the therapy, F₂(x) is the corresponding risk score of the therapy, W₁ is a weighting of the efficacy, and W₂ is a weighting of the risk score. By finding the maximum of the composite cost function, the corresponding solution x will be the best therapy in the list (i.e., the therapy with maximum efficacy and minimum risk). While using a composite cost function is one example of selecting the recommended therapy to maximize efficiency and minimize risk, it will be appreciated that other suitable methods may also be used to select the recommended therapy.

In some example embodiments, a risk score threshold may be employed in determining a recommended therapy. For example, the patient's current therapy may be recommended if the risk score is below the risk score threshold. If the risk score of the patient's current therapy is above the risk score threshold, a new therapy may be recommended to the patient. The new therapy may be recommended using the composite cost function described above or any other suitable method.

At 104 the actual severity of the patient's condition during therapy is evaluated. In an embodiment, the actual severity of the patient's condition is evaluated based on monitored biometrics during a specified monitoring period. For example, objective RLS physiological measures such as, without limitation, movement intensity, duration, and frequency may be monitored using one or more sensors. The physiological measures may be monitored while the recommended therapy is applied to the patient. For example, the physiological measures may be monitored at night when the therapy is applied to the patient. From the monitored physiological measures, the actual severity of the patient's condition may be evaluated.

At 106, the efficacy of the therapy is evaluated. The efficacy of the therapy may be based on a number of factors such as, without limitation, a reduction score of severity, a sleep quality score, and an alertness score. The reduction score may include a comparison of the baseline severity (e.g., determined before therapy) and the actual severity (e.g., determined while a therapy is in use). The reduction score may be based on a ratio of the actual to baseline severity or a difference between the baseline and actual severity. In an example embodiment, the reduction score may be within a range of 0 to 100, with a score of 100 being the most effective.

The sleep quality score may be based on a number of sleep metrics such as, without limitation, sleep efficiency, wake after sleep onset, number of awakenings, sleep onset latency, deep sleep percentage, and total sleep time. The sleep metrics may be monitored while the therapy is in use. In an example embodiment, the sleep quality score may be within a range of 0 to 100, with a score of 100 being the most effective.

The alertness score may be based on a number of metrics such as, without limitation, number of naps, duration of naps, movements, movement intensity, and behaviors. The metrics for the alertness score may be monitored during the day. In an example embodiment, the alertness score may be within a range of 0 to 100, with a score of 100 being the most effective.

While a severity reduction score, sleep quality score, and alertness score are provided as examples of factors that can be used to evaluate the efficacy of a therapy, it will be appreciated that different factors may be employed. The various factors may be weighted differently to determine the therapy efficacy. For example, in an embodiment, the reduction score and sleep quality score are weighted higher than the alertness score. However, it will be appreciated that different weightings may be used without departing from the disclosed concept.

At 108, the risk score of the therapy is evaluated. As part of monitoring the patient, factors associated with the risk score may be monitored. Factors associated with the risk score may include therapy side effects (e.g., dizziness, somnolence, nausea, etc.), therapy augmentation metrics (e.g., movement intensity, duration and frequency), and relevant stressors (e.g., health condition changes and medication changes). In an embodiment, the side effects may be monitored via subjective patient inputs such as text inputs provided to a chat bot, web chat bubble, or other suitable input mechanism, and processed using natural language processing or other suitable processing mechanisms.

For initial risk scores, population data such as clinical guidelines or clinical studies may be used to determine the risk score. As the patient is continuously monitored and uses therapies, the initial risk scores may be replaced with personalized risk scores based on the monitoring and risk evaluation of the patient.

At 110, the therapy efficacy-risk map is updated. In an embodiment, the efficacy and/or risk scores evaluated based on monitoring of the patient replace their corresponding existing efficacy and/or risk scores in the therapy efficacy-risk map. In this manner, over time as the patient is monitored and uses therapies, the initial therapy efficacy-risk map based on population data is transformed into a personalized efficacy-risk map. The personalized therapy efficacy-risk map captures efficacy and risks of therapies personal to the patient, such as when a therapy is more or less effective or present more or less risk to the patient than what the population data suggests. Additionally, through continuous monitoring and evaluation, changes in efficacy and risk of therapies over time are captured in the therapy efficacy-risk map, such as when augmentation begins to appear after using a therapy for a time or when a therapy loses some efficacy after a period of use. In this manner, the therapy recommended to the patient can continually be optimized.

In some embodiments, one or more steps of the method may be omitted or modified. For example, the method may have a build phase where therapies are recommended by a therapy provider. As the patient uses therapies, the efficacies and risks of the therapies may be evaluated through monitoring of the patient and the therapy efficacy-risk map for the patient evolves from a default therapy-severity map to a personalized therapy-severity map. Over time, as the patient has tried different therapies, the method may move to a deployment phase where the personalized therapy efficacy-risk map is used to recommend a therapy.

The method may be implement by one or more computing devices, memories, and sensors. For example, monitoring the behaviors and/or biometrics of the patient may be implemented with sensors such as under mattress and/or wearable sensors as well as any suitable computing device for inputting subjective patient responses. The method may be implemented on a localized or distributed system. For example, one or more parts of the method may be implemented on a user device, such as a mobile phone, tablet, or computer, as well, parts of the method may be implemented on a remote device such as a server. For example, the personalized therapy efficacy-risk map may be stored on the user device or on a server. Some examples of systems for implementing parts of the method will be described in more detail herein.

FIG. 2 is schematic diagram of a system 200 for optimizing pharmacotherapy in accordance with an example embodiment of the disclosed concept. The system 200 includes an alertness sensing module 202, a sleep metrics sensing module 204, a baseline RLS severity module 206, an RLS metrics monitoring module 208, a selected therapy module 210, a therapy efficacy evaluation module 212, an actual RLS severity evaluation module 214, a build personalized therapy efficacy-risk map module 216, and an RLS therapy risk evaluation module 218.

The alertness sensing module 202 may sense metrics associated with alertness such as nap frequency, nap duration, movement, movement intensity, and behaviors. The alertness sensing module 202 may include or receive outputs from one or more sensors such as under mattress and/or wearable sensors. The sleep metrics sensing module 204 may sense metrics associated with sleep quality such as sleep efficiency, wake after sleep onset, sleep onset latency, total sleep time, number of awakenings, and sleep stages. The sleep metrics sensing module may include or receive outputs from one or more sensors such as under mattress and/or wearable sensors. The RLS metrics monitoring module 208 may sense objective metrics associated with RLS such as leg movement frequency, intensity, and duration. The RLS metrics monitoring module 208 may include or receive output from one or more sensors such as under mattress and/or wearable sensors. Together, the alertness sensing module 202, sleep metrics sensing module 204, and RLS metrics monitoring module 208 may be used to monitor biometrics of a patient.

The baseline RLS severity module 206 is structured to estimate a baseline severity of the patient's RLS. In an embodiment, the baseline severity may be defined by pre-determined levels (i.e., mild, moderate, severe, and very severe) based on the International RLS Rating Scale. Objective RLS related physiological measures such as movement intensity, duration, and frequency may be used to determine the patient's RLS severity level. The baseline RLS severity module 206 may include or be connected to one or more sensors structured to monitor the objective RLS related physiological measures.

The selected therapy module 210 may identify the selected therapy for the patient. For example, the selected therapy module 210 may receive information on the selected therapy. The actual RLS severity module 214 is structured to estimate the actual severity of the patient's RLS. The actual severity may be estimated based on the objective RLS metrics monitored by the RLS metrics monitoring module 208.

The therapy efficacy evaluation module 212 is structured to evaluate the efficacy of the therapy. The efficacy may be evaluated as described above with respect to FIG. 1. The RLS therapy risk evaluation module 218 is structured to evaluate the risk of the selected therapy. The risk may be evaluated as described above with respect to FIG. 1.

The build personalized therapy efficacy-risk map module 216 is structured to build a personalized therapy risk-efficacy map for the patient based on the actual severity, the selected therapy, the evaluated efficacy of the therapy, and the evaluated risk of the therapy. As described above, the therapy efficacy-risk map corresponds therapies with levels of severity and includes an evaluation of the efficacy of the therapy and an evaluation of the risk of the therapy. The initial therapy efficacy-risk map may be a default map generated by population data. Over time, the therapy efficacy-risk map may be updated based on monitoring and evaluation of the patient to become a personalized therapy efficacy-risk map.

The system 200 may be implemented as one or more computing devices and sensors. The system 200 may be a localized or distributed system.

In some embodiments, the selected therapy module 210 may also store recommendations for therapy. The recommendation may be based on maximizing efficacy and minimizing risk, as is described above with respect to FIG. 1.

FIG. 3 is a schematic diagram of a system 300 in a build phase for the personal risk history of pharmacotherapy in accordance with an example embodiment of the disclosed concept. The system 300 includes an RLS side effects monitoring module 302, an RLS augmentation metrics monitoring module 304, a stressor sensing module 306, a population risk history module 308, an RLS therapy risk evaluation module 310, and a build personal risk history module 312.

The RLS side effects monitoring module 302 is structured to monitor side effects associated with the selected therapy. The RLS side effects monitoring module 302 may include or be connected to one or more devices structured to obtain subjective inputs from the patient associated with side effects. For example and without limitation, the devices may include one more computing devices with a user interface to prompt a user to provide subjective inputs such as free text inputs (e.g., via a chat bot or web chat bubble) related to side effects. The devices may also include a natural language processing module to process the inputs. For example, the devices may be structured to prompt a user to answer a question associated with side effects and to process the answer to determine whether a side effect is present.

The RLS augmentation metrics monitoring module 304 is structured to monitor metrics associated with augmentation associated with the selected therapy. The augmentation metrics may include, without limitation, movement intensity, duration and frequency. The RLS augmentation metrics monitoring module may include or be connected to one or more sensors structured to monitor the augmentation metrics.

The stressor sensing module 306 is structured to monitor stressors associated with the selected therapy. The stressors may include, without limitation, health condition changes and medication changes. The stressor sensing module 306 may include or be connected to one or more sensors structured to monitor the stressors. The stressor sensing module 306 may also include or be connected to one or more devices structured to receive inputs associated with the stressors.

The population risk history module 308 is structured to gather and/or provide a risk history of a selected therapy from population data such as clinical studies or guidelines. As has been described herein, an initial risk score associated with a therapy may be generated from population data. The population risk history module 308 may include or be connected to a storage device including risks associated with therapies.

The RLS therapy risk evaluation module 310 is structured to determine a risk score of a therapy based on one or more of the side effects, augmentation metrics, stressors, and risk history. The build personal risk history module 312 is structured to build and store a personal risk history based on risk scores determined by the RLS therapy risk evaluation module 310. For example, the build personal risk history module 312 may include or be connected to a storage device that stores therapies and their associated risk scores. Initially, the build personal risk history module 312 may use an initial risk history based on population data. As the patient is monitored and evaluated, the build personal risk history module 312 may update the initial risk history by replacing initial risk scores with risk scores determined by the RLS therapy risk evaluation module.

The system 300 may be implemented as one or more computing devices and sensors. The system 300 may be a localized or distributed system. In an embodiment, the system 300 may be used in a build phase when little monitoring of the patient has been performed and population risk history has to be used to evaluate therapy risk. Over time, the system 300 may transition to or be replaced by a system in a deployment phase where the personal risk history is more developed based on monitoring and evaluation of the patient. An example of a system in the deployment phase will be described in more detail with respect to FIG. 4.

In some embodiments of the disclosed concept, the system 300 may include one or more interfaces. The interfaces may be, for example and without limitations, user interfaces available in device applications to a patient and/or care provider. In some embodiments, the interfaces may provide for input from multiple sources. The sources may include direct input from the user (e.g., questionnaires, free text input, etc.), direct input from a care provider, data such as articles and studies, and input from sensors. Sensors may include sensors on a user device (e.g., temperature, time, position, weather, or other sensors), wearable sensors (e.g., watch, ring, patch, etc.), or other sensors positioned within a home or other area (e.g., temperature, mattress, blanket, pillow, etc.). The sensors may be used to gather real-time data associated with the patient and their environment. The information from multiple sources may be used to continuously monitor the patient for symptoms and exacerbations associated with their condition and/or their current therapy. The system 300 may also include one or more storage and processing device to store and evaluate the collected data.

FIG. 4 is a schematic diagram of a system 400 in a deployment phase for evaluating risk of pharmacotherapy in accordance with an example embodiment of the disclosed concept. The system includes an RLS side effects monitoring module 402, an RLS augmentation metrics monitoring module 404, and a stressor sensing module 406 which operate similar to their corresponding components in FIG. 3. The system 400 also includes a personal risk history module 408 and an RLS therapy risk evaluation module 410.

The personal risk history module 408 is structured to store a personal risk history associated with the patient. The personal risk history may be the same or similar to one built using the system 300 of FIG. 3, and includes risk scores determined based on monitoring and evaluation of the patient. The RLS therapy risk evaluation module 410 is structured to determine a risk score associated with a selected therapy based on one or more of the side effects, augmentation metrics, stressors, and the personal risk history. The RLS therapy risk evaluation module 410 is also structured to update the personal risk history with each new determined risk score. In this manner, the personal risk history is continually update as the patient is monitored and evaluated while utilizing therapies.

The risk scores determined by the system 400 may be employed in the systems or methods of FIG. 1 or 2 to build and/or update the therapy efficacy-risk map. For example, the risk score determined based on monitoring and evaluation of the patient may be used to replace an existing risk score in the therapy efficacy-risk map.

While some embodiments have been described above related to optimizing treatment of RLS, it will be appreciated that the disclosed concept is also applicable to treatment of other conditions. Additionally, while some embodiments have been described in association with pharmacotherapy, it will be appreciated that the disclosed concept may also be applied to different types of therapies.

It will also be appreciated that an embodiment of the disclosed concept may be embodied on a non-transitory computer readable medium storing one or more programs, including instructions, which when executed by a computer, causes the computer to perform the method described with respect to FIG. 1.

Although the invention has been described in detail for the purpose of illustration based on what is currently considered to be the most practical and preferred embodiments, it is to be understood that such detail is solely for that purpose and that the invention is not limited to the disclosed embodiments, but, on the contrary, is intended to cover modifications and equivalent arrangements that are within the spirit and scope of the appended claims. For example, it is to be understood that the present invention contemplates that, to the extent possible, one or more features of any embodiment can be combined with one or more features of any other embodiment.

In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word “comprising” or “including” does not exclude the presence of elements or steps other than those listed in a claim. In a device claim enumerating several means, several of these means may be embodied by one and the same item of hardware. The word “a” or “an” preceding an element does not exclude the presence of a plurality of such elements. In any device claim enumerating several means, several of these means may be embodied by one and the same item of hardware. The mere fact that certain elements are recited in mutually different dependent claims does not indicate that these elements cannot be used in combination. 

What is claimed is:
 1. A method of optimizing pharmacotherapy, the method comprising: monitoring one or more characteristics of a patient associated with a condition of the patient and evaluating a baseline severity of the condition of the patient; recommending a selected therapy to the patient based on a therapy efficacy-risk map; evaluating an actual severity of the condition of the patient; evaluating an efficacy of the selected therapy; evaluating a risk score of the selected therapy; and updating the therapy efficacy-risk map based on the evaluated efficacy and risk score of the selected therapy.
 2. The method of claim 1, wherein the therapy efficacy-risk map corresponds one or more therapies with one or more levels of severity of the condition and includes efficacies and risk scores of the one or more therapies, and wherein recommending the selected therapy includes selecting a therapy from the therapy efficacy-risk map corresponding to the severity of the condition of the patient which maximizes efficacy and minimizes risk among the therapies corresponding to the severity of the condition of the patient.
 3. The method of claim 2, wherein updating the therapy efficacy-risk map includes replacing the efficacy associated with the selected therapy with the evaluated efficacy and replacing the risk score associated with the selected therapy with the evaluated risk score.
 4. The method of claim 2, wherein the therapy efficacy-risk map is a default map based on population data, and wherein updating the therapy efficacy-risk map includes replacing the efficacy and risk score associated in the default therapy efficacy-risk map with the selected therapy with the evaluated efficacy and evaluated risk score.
 5. The method of claim 2, wherein the therapy that maximizes efficacy and minimizes risk is determined using a composite cost function.
 6. The method of claim 1, wherein evaluating the efficacy of the therapy is based on one or more of a comparison of the predicted severity of the condition to the actual severity of the condition, a sleep quality score, and an alertness score.
 7. The method of claim 6, wherein evaluating the efficacy of the therapy is based on a comparison of the baseline severity of the condition to the actual severity of the condition, a sleep quality score, and an alertness score, and wherein each of the comparison of the baseline severity of the condition to the actual severity of the condition, the sleep quality score, and the alertness score has an associated weighting.
 8. The method of claim 1, wherein monitoring one or more of the characteristics of the patient includes monitoring one or more objective physiological characteristics associated with Restless Legs Syndrome (RLS), and wherein evaluating the actual severity of the condition is based on the one or more objective physiological characteristics.
 9. The method of claim 1, wherein monitoring one or more characteristics of the patient includes monitoring one or more of side effects, augmentation metrics, and stressors, and wherein evaluating the risk score of the selected therapy is based on one or more of the monitored side effects, augmentation metrics, and stressors.
 10. The method of claim 9, wherein the side effects are monitored using subjective inputs from the patient and natural language processing of subjective inputs.
 11. The method of claim 1, recommending the selected therapy to the patient includes determining a risk score associated with a current therapy of the patient and selecting the current therapy of the patient if the risk score associated with the current therapy is below a risk score threshold.
 12. The method of claim 1, wherein the selected therapy includes a drug type, a dosage, and a dose time.
 13. The method of claim 1, wherein the condition is RLS.
 14. A system for optimizing pharmacotherapy, the system comprising: one or more sensing modules structured to monitor one or more characteristics of a patient associated with a condition of the patient; a baseline severity module structured to determine a severity of the condition of the patient; a selected therapy module structured to store a recommended therapy to the patient based on a therapy efficacy-risk map; an actual severity evaluation module structured to evaluate an actual severity of the condition of the patient; a therapy efficacy evaluation module structured to evaluate an efficacy of the recommended therapy; a risk evaluation module structured to evaluate a risk score of the recommended therapy; and a therapy efficacy-risk map update module structured to update the therapy efficacy-risk map based on the evaluated efficacy and risk score of the recommended therapy.
 15. A non-transitory computer readable medium storing one or more programs, including instructions, which when executed by a computer, causes the computer to perform a method of optimizing pharmacotherapy, the method comprising: monitoring one or more characteristics of a patient associated with a condition of the patient and evaluating a baseline severity of the condition of the patient; recommending a selected therapy to the patient based on a therapy efficacy-risk map; evaluating an actual severity of the condition of the patient; evaluating an efficacy of the selected therapy; evaluating a risk score of the selected therapy; and updating the therapy efficacy-risk map based on the evaluated efficacy and risk score of the selected therapy. 